Career selection is a pivotal decision for students, particularly within the dynamic landscape of the technology sector. This study investigates the application of machine learning algorithms to improve career decision-making for information systems (IS) students. Traditional career counseling approaches often fail to provide precise and individualized guidance, necessitating the integration of artificial intelligence to enhance recommendation accuracy. This research employs machine learning models, including random forest, XGBoost, and support vector classification (SVC), to assess students’ competencies, academic achievements, and extracurricular involvement. The results demonstrate that these models achieve a balance between precision and recall in career predictions, with F1 scores of 0.8446, 0.8050, and 0.7921, respectively. Through comprehensive data analysis, these models present an effective strategy for mitigating career indecision by offering data-driven, customized recommendations, thereby assisting students in making well-informed career choices.